Typically, classificatioN objects in a domain is performed using features extracted from an analysis window which is scanned across the domain, known as a sequential search. A sequential search can be very computationally intensive, especially if a small window is used since a classification must be performed at each window position. Conventional approaches to reduce the computational load in the visual image domain are based on reducing the search space by using another sensor such as radar to cue the vision system and measure the range of the object. Limitations of the radar approach include high cost, false alarms, the need to associate radar tracks with visual objects, and overall system complexity. Alternatively, previous vision-only approaches have utilized motion-based segmentation using background estimation methods to reduce the search space by generating areas of interest (AOI) around moving objects and/or using stereo vision to estimate range in order to reduce searching in scale. This method adds cost and complexity by requiring additional cameras and computations. Motion-based segmentation is also problematic under challenging lighting conditions or if background motion exists as is the case for moving host platforms.
Additionally, there have been attempts to use Genetic Algorithms (GAs) and Evolutionary Algorithms for object detection. Genetic algorithms have been used before for decreasing the search space in vision systems. These systems employ a population of individual solutions that crossover and mutate in an effort to maximize the fitness function. Other efforts have used GAs for training and adapting neural networks to recognize objects. However, all of the currently available methods are computationally costly, and largely ineffective.
Thus, a continuing need exists for an effective and efficient object recognition system for classifying objects in a domain.